© 2011

Genetic Programming Theory and Practice IX

  • Rick Riolo
  • Ekaterina Vladislavleva
  • Jason H. Moore

Part of the Genetic and Evolutionary Computation book series (GEVO)

Table of contents

  1. Front Matter
    Pages i-xxvii
  2. Lee Spector, Kyle Harrington, Brian Martin, Thomas Helmuth
    Pages 1-16
  3. Moshe Sipper
    Pages 17-36
  4. Joel Lehman, Kenneth O. Stanley
    Pages 37-56
  5. James McDermott, Edgar Galván-Lopéz, Michael O’Neill
    Pages 57-76
  6. Cassio Pennachin, Moshe Looks, J. A. de Vasconcelos
    Pages 97-112
  7. Frank Neumann, Una-May O’Reilly, Markus Wagner
    Pages 113-128
  8. Michael F. Korns
    Pages 129-151
  9. Jason H. Moore, Douglas P. Hill, Jonathan M. Fisher, Nicole Lavender, La Creis Kidd
    Pages 153-171
  10. Pierre-Luc Noel, Kalyan Veeramachaneni, Una-May O’Reilly
    Pages 173-194
  11. Philip D. Truscott, Michael F. Korns
    Pages 195-210
  12. Back Matter
    Pages 261-263

About this book


These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics include: modularity and scalability; evolvability; human-competitive results; the need for important high-impact GP-solvable problems;; the risks of search stagnation and of cutting off paths to solutions; the need for novelty; empowering GP search with expert knowledge; In addition, GP symbolic regression is thoroughly discussed, addressing such topics as guaranteed reproducibility of SR; validating SR results, measuring and controlling genotypic complexity; controlling phenotypic complexity; identifying, monitoring, and avoiding over-fitting; finding a comprehensive collection of SR benchmarks, comparing SR to machine learning. This text is for all GP explorers. Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.


FFX automatic programming computational complexity evolutionary games genetic programming novelty search program induction sensory evaluation modeling symbolic regression tag-based reference time series prediction

Editors and affiliations

  • Rick Riolo
    • 1
  • Ekaterina Vladislavleva
    • 2
  • Jason H. Moore
    • 3
  1. 1., Center for the Study of ComplexUniversity of MichiganAnn ArborUSA
  2. 2.Evolved Analytics Europe BVBAWijnegemBelgium
  3. 3., Institute for QuantitativeDartmouth Medical SchoolLebanonUSA

Bibliographic information

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